Titlebook: Big Data Analytics and Knowledge Discovery; 23rd International C Matteo Golfarelli,Robert Wrembel,Ismail Khalil Conference proceedings 2021

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Contextual and Behavior Factors Extraction from Pedestrian Encounter Scenes Using Deep Language Mode that describes the environment and pedestrian behaviors at the pedestrian encounter scenes. The performance is compared against a baseline BiLSTM-CRF model trained for each factor separately. The evaluation results show that the proposed NLP framework outperforms the baseline model. We show that th

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Impact of Textual Data Augmentation on Linguistic Pattern Extraction to Improve the Idiomaticity of sis shows that the process of writing summaries should take into account not only terminological and collocational extraction, but also a range of linguistic material referred to here as the “support lexicon”, that plays an important role in the cognitive organisation of the field. On this basis, th

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Fabric

Efficient Graph Analytics in Python for Large-Scale Data Sciencekages for graph analytics. However, those packages are either too specific or cannot work on graphs that cannot fit into the main memory. Moreover, it is hard to handle new graph algorithms or even customize existing ones according to the analyst’s need. In this paper, we develop a general graph C++

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https://doi.org/10.1007/978-1-4842-3252-1processing, based on a genetic optimization problem formulation. Our approach intensively rewrites queries by exploiting different dimension hierarchies that may exist among dimension attributes, skipping irrelevant data to improve I/O performance. We present an experimental validation on a Spark SQL parallel cluster, showing promising results.

heirloom

Linux Distro Selection Criteriaroposed model yields significant improvements over existing methods on the benchmark VCR dataset. Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning. A Python implementation of our mechanism is publicly available at .

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